IP Library › Granted Patent US 12,277,268
Granted Patent B2
US 12,277,268 · App. 18/473,228 · Granted Apr 15, 2025

Image cropping based on eye position for a video-based eye tracker

Inventors: Gil Thieberger (Kiryat Tivon, IL); Ari M Frank (Haifa, IL)
Assignee: Facense Ltd.
G06F3/013A61B5/0205A61B5/02427A61B5/02438A61B5/14546A61B5/1455A61B5/6803G06V10/141G06V40/166G06V40/174H04N23/611H04N23/651H04N23/951A61B5/02416A61B5/7221H04N25/46
View Patent ↗
Loading inventors, assignments & file history…
Monitor This Case
Get email alerts when status or documents change.
Order Certified Copies
Most orders are placed with the USPTO same day — all within 24 business hours.
Order via The Patent Place →
Pre-filled with this patent's details
Quick Facts
Patent No.
US 12,277,268
App. No.
18/473,228
Granted
Apr 15, 2025
Kind
B2
Abstract

Systems, methods, and computer programs for eye tracking. In one embodiment, an eye tracking system includes a head-mounted device that takes measurements indicative of a position of the eye of a user. A head-mounted camera captures an image of the eye. A computer calculates the position of the eye based on the measurements, utilizes the position of the eye to crop the image around the pupil, and provides a cropped image to a video-based eye tracker. Optionally, the size of the cropped image is less than a third of the size of its respective uncropped image. Optionally, the head-mounted device includes one or more of the following: a photosensor-oculography device (PSOG), an electrooculography device (EOG), an electromyography device (EMG), an optical flow sensor, and a range sensor. Optionally, the image-based eye tracker calculates, based on the cropped image, at least one of pupil diameter and pupillary response.

Claims (37)

1. An eye tracking system comprising:

a head-mounted device configured to take measurements indicative of a position of an eye of a user (eye position);

a head-mounted camera configured to capture an image of the eye; and

a computer configured to:

calculate the eye position based on the measurements;

utilize the eye position to crop the image around the eye's pupil;

provide the cropped image to a video-based eye tracker; and

wherein calculating the eye position based on the measurements is performed at an average rate that is higher than an average rate at which images are captured by the head-mounted camera.

2. The eye tracking system of claim 1 , wherein the device comprises a photosensor-oculography device (PSOG), the measurements are of reflections of light emitted by the PSOG towards the eye, and calculating the eye position based on the measurements is performed at a rate that is at least ten times higher than the rate at which images are captured by the head-mounted camera.

3. The eye tracking system of claim 2 , further comprising head-mounted light sources configured to emit light that generates glints on the eye; and wherein the computer is further configured to utilize the eye position to select a subset of the light sources that are expected to generate one or more glints on the cornea, and operate the subset of the light sources at a higher intensity compared to the rest of the light sources.

4. The eye tracking system of claim 2 , further comprising head-mounted light sources configured to emit light that generates glints on the eye; and wherein the computer is further configured to: calculate positions of the eyelids based on the measurements of the reflections, utilize the positions of the eyelids to select a subset of the light sources that are expected to generate one or more glints on an area of the cornea not covered by the eyelids, and operate the subset of the light sources at a higher intensity compared to the rest of the light sources.

5. The eye tracking system of claim 1 , wherein size of the cropped image is less than a third of size of its respective uncropped image.

6. The eye tracking system of claim 1 , wherein the cropped image covers an area that is not greater than two times the area of a square that surrounds the iris tightly.

7. The eye tracking system of claim 1 , wherein the video-based eye tracker is configured to calculate, based on the cropped image, at least one of pupil diameter and pupillary response.

8. The eye tracking system of claim 1 , wherein the device comprises an electrooculography device, the measurements comprise a value of an electrical potential between electrodes placed close to the eye, and calculating the eye position based on the measurements is performed at a rate that is at least ten times higher than the rate at which images are captured by the head-mounted camera.

9. The eye tracking system of claim 8 , further comprising head-mounted light sources configured to emit light that generates glints on the eye; and wherein the computer is further configured to utilize the eye position to select a subset of the light sources that are expected to generate one or more glints on the cornea, and operate the subset of the light sources at a higher intensity compared to the rest of the light sources.

10. The eye tracking system of claim 1 , wherein the device comprises an electromyography device, the measurements comprise a value of an electrical potential generated by muscle cells, and calculating the eye position based on the measurements is performed at a rate that is at least ten times higher than the rate at which images are captured by the head-mounted camera.

11. The eye tracking system of claim 10 , further comprising head-mounted light sources configured to emit light that generates glints on the eye; and wherein the computer is further configured to utilize the eye position to select a subset of the light sources that are expected to generate one or more glints on the cornea, and operate the subset of the light sources at a higher intensity compared to the rest of the light sources.

12. The eye tracking system of claim 1 , wherein the device comprises an optical flow sensor, the measurements comprise values of optical flow and/or visual motion, the eye position is calculated based on an optical flow algorithm, and calculating the eye position based on the measurements is performed at a rate that is at least ten times higher than the rate at which images are captured by the head-mounted camera.

13. The eye tracking system of claim 12 , further comprising head-mounted light sources configured to emit light that generates glints on the eye; and wherein the computer is further configured to utilize the eye position to select a subset of the light sources that are expected to generate one or more glints on the cornea, and operate the subset of the light sources at a higher intensity compared to the rest of the light sources.

14. The eye tracking system of claim 1 , wherein the device comprises a range sensor, the measurements comprise a value of a range between the range sensor and the eye, and calculating the eye position based on the measurements is performed at a rate that is at least ten times higher than the rate at which images are captured by the head-mounted camera.

15. The eye tracking system of claim 14 , further comprising head-mounted light sources configured to emit light that generates glints on the eye; and wherein the computer is further configured to utilize the eye position to select a subset of the light sources that are expected to generate one or more glints on the cornea, and operate the subset of the light sources at a higher intensity compared to the rest of the light sources.

16. A method comprising:

taking, with a head-mounted device, measurements indicative of a position of an eye of a user (eye position);

capturing an image of the eye by a head-mounted camera;

calculating the eye position based on the measurements at an average rate that is higher than an average rate at which images are captured by the head-mounted camera;

utilizing the eye position for cropping the image around the eye's pupil; and

providing the cropped image to a video-based eye tracker.

17. The method of claim 16 , wherein the device comprises a photosensor-oculography device (PSOG), the measurements are of reflections of light emitted by the PSOG towards the eye, and calculating the eye position based on the measurements is performed at a rate that is at least ten times higher than the rate at which images are captured by the head-mounted camera.

18. The method of claim 16 , wherein the device comprises an electrooculography device, the measurements comprise a value of an electrical potential between electrodes placed close to the eye, and calculating the eye position based on the measurements is performed at a rate that is at least ten times higher than the rate at which images are captured by the head-mounted camera.

19. The method of claim 16 , wherein the device comprises an electromyography device, the measurements comprise a value of an electrical potential generated by muscle cells, and calculating the eye position based on the measurements is performed at a rate that is at least ten times higher than the rate at which images are captured by the head-mounted camera.

20. A non-transitory computer readable medium storing one or more computer programs configured to cause a processor-based system to execute steps comprising:

taking, with a head-mounted device, measurements indicative of a position of an eye of a user (eye position);

capturing an image of the eye by a head-mounted camera;

calculating the eye position based on the measurements at an average rate that is higher than an average rate at which images are captured by the head-mounted camera;

utilizing the eye position for cropping the image around the eye's pupil; and

providing the cropped image to a video-based eye tracker.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Sep 26, 2023
From: THIEBERGER, GIL; FRANK, ARI M
To: FACENSE LTD
Reel/Frame 065026/0435 →
Continuity (5)
Continuation 17490432 · Sep 30, 2021
Provisional Application 63140453 · Jan 22, 2021
Provisional Application 63122961 · Dec 9, 2020
Provisional Application 63113846 · Nov 14, 2020
Related Publication 20240012476A1 · Jan 11, 2024
References Cited (109)
US 6542081B2 · Torch · 2003 [cited by applicant]
US 7515054B2 · Torch · 2009 [cited by applicant]
US 9042615B1 · Bradley · 2015 [cited by applicant]
US 9101296B2 · Schroeder et al. · 2015 [cited by applicant]
US 9489817B2 · Gui · 2016 [cited by applicant]
US 9600069B2 · Publicover et al. · 2017 [cited by applicant]
US 9696859B1 · Heller · 2017 [cited by applicant]
US 9766699B2 · Skogo et al. · 2017 [cited by applicant]
US 9866754B2 · Eskilsson et al. · 2018 [cited by applicant]
US 10152869B2 · Peyrard · 2018 [cited by applicant]
US 10231614B2 · Krueger · 2019 [cited by applicant]
US 10317672B2 · Sarkar · 2019 [cited by applicant]
US 10401952B2 · Young · 2019 [cited by applicant]
US 10466360B1 · Bardagjy · 2019 [cited by examiner]
US 10466779B1 · Liu · 2019 [cited by applicant]
US 10650533B2 · Mallinson · 2020 [cited by applicant]
US 10757328B2 · Thukral · 2020 [cited by applicant]
US 10788894B2 · Price · 2020 [cited by applicant]
US 10795435B2 · Fontanel · 2020 [cited by applicant]
US 20120069301A1 · Hirata · 2012 [cited by applicant]
US 20150199005A1 · Haddon · 2015 [cited by applicant]
US 20150335278A1 · Ashmore · 2015 [cited by applicant]
US 20160167672A1 · Krueger · 2016 [cited by applicant]
US 20160342835A1 · Kaehler · 2016 [cited by applicant]
US 20170000339A1 · Di Statsi · 2017 [cited by applicant]
US 20170219833A1 · Mayama · 2017 [cited by applicant]
US 20180068449A1 · Malaika et al. · 2018 [cited by applicant]
US 20180088340A1 · Amayeh et al. · 2018 [cited by applicant]
US 20180184002A1 · Thukral · 2018 [cited by applicant]
US 20180196509A1 · Trail · 2018 [cited by examiner]
US 20180210547A1 · Sarkar · 2018 [cited by applicant]
US 20180284872A1 · Schluessler · 2018 [cited by applicant]
US 20180299953A1 · Selker et al. · 2018 [cited by applicant]
US 20190174039A1 · Jung et al. · 2019 [cited by applicant]
US 20190196179A1 · Sarkar et al. · 2019 [cited by applicant]
US 20190204912A1 · Yang et al. · 2019 [cited by applicant]
US 20190204913A1 · Sarkar et al. · 2019 [cited by applicant]
US 20190350471A1 · Marks et al. · 2019 [cited by applicant]
US 20200026350A1 · Eash et al. · 2020 [cited by applicant]
US 20200121184A1 · Hu · 2020 [cited by applicant]
US 20200129063A1 · McGrath · 2020 [cited by examiner]
US 20200278539A1 · Petljanski et al. · 2020 [cited by applicant]
US 20200285050A1 · Price et al. · 2020 [cited by applicant]
US 20200285307A1 · Price et al. · 2020 [cited by applicant]
US 20200285848A1 · Price et al. · 2020 [cited by applicant]
US 20200311978A1 · Aflaki Beni · 2020 [cited by applicant]
US 20200335032A1 · Kiik · 2020 [cited by applicant]
US 20200389582A1 · Herman · 2020 [cited by applicant]
US 20220050522A1 · Krukowski · 2022 [cited by examiner]
US 20230009372A1 · Macknik · 2023 [cited by applicant]
US 20230324989A1 · Krukowski · 2023 [cited by examiner]
WO WO2019067731 · 2019 [cited by applicant]
Abdulin, Evgeniy R., and Oleg V. Komogortsev. “Study of Additional Eye-Related Features for Future Eye-Tracking Techniques.” [cited by applicant]
Akşit, Kaan, Jan Kautz, and David Luebke. “Gaze-sensing leds for head mounted displays.” [cited by applicant]
Angelopoulos, Anastasios N., et al. “Event based, near eye gaze tracking beyond 10,000 Hz.” [cited by applicant]
Ariz, Mikel, et al. “Optimizing the interoperability between a VOG and a EMG system.” [cited by applicant]
Brunyé, Tad T., et al. “A review of eye tracking for understanding and improving diagnostic interpretation.” [cited by applicant]
Chin, Craig A., et al. “Integrated electromyogram and eye-gaze tracking cursor control system for computer users with motor disabilities.” (2008). [cited by applicant]
Choo, Kyojin D., et al. “5.2 energy-efficient low-noise CMOS image sensor with capacitor array-assisted charge-injection SAR ADC for motion-triggered low-power IoT applications.” [cited by applicant]
Cognolato, Matteo, Manfredo Atzori, and Henning Müller. “Head-mounted eye gaze tracking devices: An overview of modern devices and recent advances.” [cited by applicant]
Cristina, Stefania, and Kenneth P. Camilleri. “Unobtrusive and pervasive video-based eye-gaze tracking.” [cited by applicant]
Dietz, Paul, William Yerazunis, and Darren Leigh. “Very low-cost sensing and communication using bidirectional LEDs.” [cited by applicant]
Dutta, Anjan, et al. “Vision tracking: A survey of the state-of-the-art.” [cited by applicant]
Fuhl, Wolfgang, et al. “Pupil detection for head-mounted eye tracking in the wild: an evaluation of the state of the art.” [cited by applicant]
Gilzenrat, Mark S., et al. “Pupil diameter tracks changes in control state predicted by the adaptive gain theory of locus coeruleus function.” [cited by applicant]
Goni, Sonia, et al. “Robust algorithm for pupil-glint vector detection in a video-oculography eyetracking system.” [cited by applicant]
Griffith, Henry, Dmytro Katrychuk, and Oleg Komogortsev. “Assessment of shift-invariant CNN gaze mappings for PS-OG eye movement sensors.” [cited by applicant]
Horiuchi, Ryogo, Tomohito Ogasawara, and Norihisa Miki. “Fatigue assessment by blink detected with attachable optical sensors of dye-sensitized photovoltaic cells.” [cited by applicant]
Hosp, Benedikt, et al. “RemoteEye: An open-source high-speed remote eye tracker: Implementation insights of a pupil-and glint-detection algorithm for high-speed remote eye tracking.” [cited by applicant]
Johns, Murray W., et al. “Monitoring eye and eyelid movements by infrared reflectance oculography to measure drowsiness in drivers.” [cited by applicant]
Katrychuk, Dmytro, Henry K. Griffith, and Oleg V. Komogortsev. “Power-efficient and shift-robust eye-tracking sensor for portable VR headsets.” [cited by applicant]
Katrychuk, Dmytro, Henry Griffith, and Oleg Komogortsev. “A Calibration Framework for Photosensor-based Eye-Tracking System.” ACM Symposium on Eye Tracking Research and Applications. 2020. [cited by applicant]
Kolakowski, Susan M., and Jeff B. Pelz. “Compensating for eye tracker camera movement.” [cited by applicant]
Li, Qiao, and Gari D. Clifford. “Dynamic time warping and machine learning for signal quality assessment of pulsatile signals.” [cited by applicant]
Li, Richard, et al. “Optical gaze tracking with spatially-sparse single-pixel detectors.” [cited by applicant]
Li, Tianxing, and Xia Zhou. “Battery-free eye tracker on glasses.” [cited by applicant]
Magdalena Nowara, Ewa, et al. “SparsePPG: Towards driver monitoring using camera-based vital signs estimation in near-infrared.” [cited by applicant]
Mantiuk, Radosław, et al. “Do-it-yourself eye tracker: Low-cost pupil-based eye tracker for computer graphics applications.” [cited by applicant]
Martins, R., and J. M. Carvalho. “Eye blinking as an indicator of fatigue and mental load—a systematic review.” [cited by applicant]
Mastrangelo, Alexander S., et al. “A low-profile digital eye-tracking oculometer for smart eyeglasses.” [cited by applicant]
Messikommer, Nico, et al. “Event-based asynchronous sparse convolutional networks.” [cited by applicant]
Mestre, Clara, Josselin Gautier, and Jaume Pujol. “Robust eye tracking based on multiple corneal reflections for clinical applications.” [cited by applicant]
Meyer, Johannes, et al. “A novel camera-free eye tracking sensor for augmented reality based on laser scanning.” [cited by applicant]
Padmanaban, Nitish, Robert Konrad, and Gordon Wetzstein. “Autofocals: Evaluating gaze-contingent eyeglasses for presbyopes.” [cited by applicant]
Pan, Liyuan, et al. “Bringing a blurry frame alive at high frame-rate with an event camera.” [cited by applicant]
Park, Seonwook, et al. “Few-shot Adaptive Gaze Estimation”, IEEE/CVF International Conference on Computer Vision (ICCV), arXiv:1905.01941 (2019). [cited by applicant]
Rigas, Ioannis, Hayes Raffle, and Oleg V. Komogortsev. “Hybrid ps-v technique: A novel sensor fusion approach for fast mobile eye-tracking with sensor-shift aware correction.” [cited by applicant]
Rigas, Ioannis, Hayes Raffle, and Oleg V. Komogortsev. “Photosensor oculography: survey and parametric analysis of designs using model-based simulation.” [cited by applicant]
Rostaminia, Soha, et al. “Ilid: low-power sensing of fatigue and drowsiness measures on a computational eyeglass.” [cited by applicant]
Russo, J. Edward. “The limbus reflection method for measuring eye position.” [cited by applicant]
Ryan, Cian, et al. “Real-time face & eye tracking and blink detection using event cameras.” [cited by applicant]
Ryan, Wayne J., Andrew T. Duchowski, and Stan T. Birchfield. “Limbus/pupil switching for wearable eye tracking under variable lighting conditions.” [cited by applicant]
Scheerlinck, Cedric, Nick Barnes, and Robert Mahony. “Asynchronous spatial image convolutions for event cameras.” [cited by applicant]
Schleicher, Robert, et al. “Blinks and saccades as indicators of fatigue in sleepiness warnings: looking tired?. ” [cited by applicant]
Shi, Lixin, et al. “Light field reconstruction using sparsity in the continuous fourier domain.” [cited by applicant]
Sree, Niranjanaa Bose S. and Kandaswamy, A. “Sparse characterization of PPG based on K-SVD for beat-to-beat blood pressure prediction”, Biomedical Research 29 (4), 835-843 (2018). [cited by applicant]
Świrski, Lech, Andreas Bulling, and Neil Dodgson. “Robust real-time pupil tracking in highly off-axis images.” [cited by applicant]
Tonsen, Marc, et al. “Invisibleeye: Mobile eye tracking using multiple low-resolution cameras and learning-based gaze estimation.” [cited by applicant]
Tonsen, Marc, Chris Kay Baumann, and Kai Dierkes. “A High-Level Description and Performance Evaluation of Pupil Invisible.” [cited by applicant]
Topal, Cihan, et al. “A low-computational approach on gaze estimation with eye touch system.” [cited by applicant]
Valliappan, Nachiappan, et al. “Accelerating eye movement research via accurate and affordable smartphone eye tracking.” [cited by applicant]
Vidal, Mélodie, et al. “Wearable eye tracking for mental health monitoring.” [cited by applicant]
Wang, Joseph Tao-yi. “Pupil dilation and eye tracking.” [cited by applicant]
Young, Laurence R., and David Sheena. “Survey of eye movement recording methods.” [cited by applicant]
Zemblys, Raimondas, et al. “Using machine learning to detect events in eye-tracking data.” [cited by applicant]
Zemblys, Raimondas, and Oleg Komogortsev. “Making stand-alone PS-OG technology tolerant to the equipment shifts.” [cited by applicant]
Zhao, Hongfan. “Micro-Scanning Mirror based Eye-tracking Technology.” (2020). [cited by applicant]
International search report, PCT/IB2021/059791, date of mailing Jan. 27, 2022. [cited by applicant]
Written opinion of the international searching authority, PCT/IB2021/059791, date of mailing Jan. 27, 2022. [cited by applicant]
Cited By (1)
US 12,551,099